Agriculture,significantly impacted by climate change and climate variability,serves as the primary livelihood for smallholder farmers in South Asia.This study aims to examine and evaluate the factors influencing small...Agriculture,significantly impacted by climate change and climate variability,serves as the primary livelihood for smallholder farmers in South Asia.This study aims to examine and evaluate the factors influencing smallholder farmers'adaptive capacity(AC)in addressing these risks through surveys from 633 households across Nepal,India,and Bangladesh.The findings reveal that AC is influenced by various indicators categorized under eight principal factors.The first three factors,which explain about one-third of the variance in each country,include distinct significant indicators for each nation:in Nepal,these indicators are landholding size,skill-development training,knowledge of improved seed varieties,number of income sources,access to markets,and access to financial institutions;in India,they encompass ac-cess to agricultural-input information,knowledge of seed varieties,access to markets,access to crop insurance,changing the sowing/harvesting times of crops,and access to financial ser-vices;in Bangladesh,the key factors are access to financial institutions,community coopera-tion,changing the sowing/harvesting times of crops,knowledge of improved seed varieties,and access to agricultural-input information.Notably,indicators such as trust in weather in-formation,changing sowing/harvesting times of crops,and crop insurance were identified as important determinants of AC,which have been overlooked in previous studies.展开更多
Data processing and climate characterisation to study its impact is becoming difficult due to insufficient and unavailable data,especially in developing countries.Understanding climate’s impact on burnt areas in Ghan...Data processing and climate characterisation to study its impact is becoming difficult due to insufficient and unavailable data,especially in developing countries.Understanding climate’s impact on burnt areas in Ghana(Guinea-savannah(GSZ)and Forest-savannah Mosaic zones(FSZ))leads us to opt for machine learning.Through Google Earth Engine(GEE),rainfall(PR),maximum temperature(Tmax),minimum temperature(Tmin),average temperature(Tmean),Palmer Drought Severity Index(PDSI),relative humidity(RH),wind speed(WS),soil moisture(SM),actual evapotranspiration(ETA)and reference evapotranspiration(ETR)have been acquired through CHIRPS(Climate Hazards group Infrared Precipitation with Stations),FLDAS dataset(Famine Early Warning Systems Network(FEWS NET)Land Data Assimilation System)and TerraClimate platform from 1991 to 2021.The objective is to analyse the link and the contribution of climatic and environmental parameters on wildfire spread in GSZ and FSZ in Ghana.Variables were analysed(area burnt and the number of activefires)through Spearman correlation and the cross-correlation function(CCF)(2001 to 2021).The tests(Mann-Kendall and Sens’s slope trend test,Pettitt test and the Lee and Heghinian test)showed the overall decrease in rainfall and increase in temperature respectively(-0.1 mm;+0.8℃)in GSZ and(-0.9 mm;+0.3℃)in FSZ.In terms of impact,PR,ETR,FDI,Tmean,Tmax,Tmin,RH,ETA and SM contribute tofire spread.Through the codes developed,researchers and decision-makers could update them at different times easily to monitor climate variability and its impact onfires.展开更多
基金The Alliance of International Science Organizations(ANSO),No.ANSO-CR-PP-2021-06The Second Tibetan Plateau Scientific Expedition and Research,No.2019QZKK0603。
文摘Agriculture,significantly impacted by climate change and climate variability,serves as the primary livelihood for smallholder farmers in South Asia.This study aims to examine and evaluate the factors influencing smallholder farmers'adaptive capacity(AC)in addressing these risks through surveys from 633 households across Nepal,India,and Bangladesh.The findings reveal that AC is influenced by various indicators categorized under eight principal factors.The first three factors,which explain about one-third of the variance in each country,include distinct significant indicators for each nation:in Nepal,these indicators are landholding size,skill-development training,knowledge of improved seed varieties,number of income sources,access to markets,and access to financial institutions;in India,they encompass ac-cess to agricultural-input information,knowledge of seed varieties,access to markets,access to crop insurance,changing the sowing/harvesting times of crops,and access to financial ser-vices;in Bangladesh,the key factors are access to financial institutions,community coopera-tion,changing the sowing/harvesting times of crops,knowledge of improved seed varieties,and access to agricultural-input information.Notably,indicators such as trust in weather in-formation,changing sowing/harvesting times of crops,and crop insurance were identified as important determinants of AC,which have been overlooked in previous studies.
文摘Data processing and climate characterisation to study its impact is becoming difficult due to insufficient and unavailable data,especially in developing countries.Understanding climate’s impact on burnt areas in Ghana(Guinea-savannah(GSZ)and Forest-savannah Mosaic zones(FSZ))leads us to opt for machine learning.Through Google Earth Engine(GEE),rainfall(PR),maximum temperature(Tmax),minimum temperature(Tmin),average temperature(Tmean),Palmer Drought Severity Index(PDSI),relative humidity(RH),wind speed(WS),soil moisture(SM),actual evapotranspiration(ETA)and reference evapotranspiration(ETR)have been acquired through CHIRPS(Climate Hazards group Infrared Precipitation with Stations),FLDAS dataset(Famine Early Warning Systems Network(FEWS NET)Land Data Assimilation System)and TerraClimate platform from 1991 to 2021.The objective is to analyse the link and the contribution of climatic and environmental parameters on wildfire spread in GSZ and FSZ in Ghana.Variables were analysed(area burnt and the number of activefires)through Spearman correlation and the cross-correlation function(CCF)(2001 to 2021).The tests(Mann-Kendall and Sens’s slope trend test,Pettitt test and the Lee and Heghinian test)showed the overall decrease in rainfall and increase in temperature respectively(-0.1 mm;+0.8℃)in GSZ and(-0.9 mm;+0.3℃)in FSZ.In terms of impact,PR,ETR,FDI,Tmean,Tmax,Tmin,RH,ETA and SM contribute tofire spread.Through the codes developed,researchers and decision-makers could update them at different times easily to monitor climate variability and its impact onfires.
